Abstract: Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio-entity2vec and bio-doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real-time. Second, it is straightforward to interpret and operationalize. Third, it doesn't require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real-time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries.

Abstract: Phellinus baumii, a fungus that grows on mulberry trees and is used in traditional Chinese medicine, exerts therapeutic effects against various diseases, including cancer. Polyphenols, generally considered to be antioxidants, have antitumor and proapoptotic effects. In this study, we identified the composition of Phellinus baumii polyphenol (PBP) and characterized its 17 chemical components by UPLC-ESI-QTOF-MS. Furthermore, to clarify the potential mechanism of PBP against Lung Cancer Cells, network pharmacology and experimental verification were combined. Molecular docking elucidated the binding conformation and mechanism of the primary active components (Osmundacetone and hispidin) to the core targets CASP3, PARP1 and TP53. In addition, potential molecular mechanisms of PBP predicted by network pharmacology analysis were validated in vitro. PBP significantly inhibited the human lung cancer A549 cells and showed typical apoptotic characteristics, without significant cytotoxicity to normal human embryonic kidney (HEK293) cells. Analysis using flow cytometry and western blot indicated that PBP caused apoptosis, cell cycle arrest, reactive oxygen species (ROS) accumulation, and mitochondrial membrane potential (MMP) depression in A549 cells to exercise its antitumor effects. These results reveal that PBP has great potential for use as an active ingredient for antitumor therapy.

Abstract: Fluvoxamine plasma concentrations have been shown to decrease throughout pregnancy. CYP2D6 polymorphisms significantly influence these changes. However, knowledge of an optimum dose adjustment according to the CYP2D6 phenotype is still limited. This study implemented a physiologically based pharmacokinetic modelling approach to assess the gestational changes in fluvoxamine maternal and umbilical cord concentrations. The optimal dosing strategies during pregnancy were simulated, and the impact of CYP2D6 phenotypes on fluvoxamine maternal and fetal concentrations was considered. A significant decrease in fluvoxamine maternal plasma concentrations was noted during gestation. As for the fetal concentration, a substantial increase was noted for the poor metabolisers (PM), with a constant level in the ultrarapid (UM) and extensive (EM) metabolisers commencing from gestation week 20 to term. The optimum dosing regimen suggested for UM and EM reached a maximum dose of 300 mg daily at gestational weeks (GW) 15 and 35, respectively. In contrast, a stable dose of 100 mg daily throughout gestation for the PM is sufficient to maintain the fluvoxamine plasma concentration within the therapeutic window (60-230 ng/mL). Dose adjustment during pregnancy is required for fluvoxamine, particularly for UM and EM, to maintain efficacy throughout the gestational period.

Abstract: Tumor necrosis factor-[Formula: see text] inhibitors (TNFi) have been a standard treatment in ulcerative colitis (UC) for nearly 20 years. However, insufficient response rate to TNFi therapies along with concerns around their immunogenicity and inconvenience of drug delivery through injections calls for development of UC drugs targeting alternative proteins. Here, we propose a multi-omic network biology method for prioritization of protein targets for UC treatment. Our method identifies network modules on the Human Interactome-a network of protein-protein interactions in human cells-consisting of genes contributing to the predisposition to UC (Genotype module), genes whose expression needs to be modulated to achieve low disease activity (Response module), and proteins whose perturbation alters expression of the Response module genes to a healthy state (Treatment module). Targets are prioritized based on their topological relevance to the Genotype module and functional similarity to the Treatment module. We demonstrate utility of our method in UC and other complex diseases by efficiently recovering the protein targets associated with compounds in clinical trials and on the market . The proposed method may help to reduce cost and time of drug development by offering a computational screening tool for identification of novel and repurposing therapeutic opportunities in UC and other complex diseases.

Abstract: The advancement of high-throughput sequencing and genomic analysis revealed that acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease. The abundance of such genetic data in ALL can also be utilized to identify potential targets for drug discovery and even drug repurposing. We aimed to determine potential genes for drug development and further guide the identification of candidate drugs repurposed for treating ALL through integrated genomic network analysis. Genetic variants associated with ALL were retrieved from the GWAS Catalog. We further applied a genomic-driven drug repurposing approach based on the six functional annotations to prioritize crucial biological ALL-related genes based on the scoring system. Lastly, we identified the potential drugs in which the mechanisms overlapped with the therapeutic targets and prioritized the candidate drugs using Connectivity Map (CMap) analysis. Forty-two genes were considered biological ALL-risk genes with ARID5B topping the list. Based on potentially druggable genes that we identified, palbociclib, sirolimus, and tacrolimus were under clinical trial for ALL. Additionally, chlorprothixene, sirolimus, dihydroergocristine, papaverine, and tamoxifen are the top five drug repositioning candidates for ALL according to the CMap score with dasatinib as a comparator. In conclusion, this study determines the practicability and the potential of integrated genomic network analysis in driving drug discovery in ALL.

Abstract: Genetic analysis of intra-host viral populations provides unique insight into pre-emergent mutations that may contribute to the genotype of future variants. Clinical samples positive for SARS-CoV-2 collected in California during the first months of the pandemic were sequenced to define the dynamics of mutation emergence as the virus became established in the state. Deep sequencing of 90 nasopharyngeal samples showed that many mutations associated with the establishment of SARS-CoV-2 globally were present at varying frequencies in a majority of the samples, even those collected as the virus was first detected in the US. A subset of mutations that emerged months later in consensus sequences were detected as subconsensus members of intra-host populations. Spike mutations P681H, H655Y, and V1104L were detected prior to emergence in variant genotypes, mutations were detected at multiple positions within the furin cleavage site, and pre-emergent mutations were identified in the nucleocapsid and the envelope genes. Because many of the samples had a very high depth of coverage, a bioinformatics pipeline, "Mappgene", was established that uses both iVar and LoFreq variant calling to enable identification of very low-frequency variants. This enabled detection of a spike protein deletion present in many samples at low frequency and associated with a variant of concern.

Abstract: Barbados has a rich traditional use of medicinal plants, especially among the older population who may have a chronic noncommunicable disease. This study aims to identify possible drug-herb interactions between popular herbal remedies used to manage elevated blood pressure and conventional antihypertensive drugs. In this study, in silico molecular docking experiments with AutoDock Vina (Scripps Research Institute, La Jolla, CA), a part of Yasara Structure software, version 20.12.24, were used to screen 30 potential phytochemicals for drug interactions from 11 popular plants in Barbados that are used for high blood pressure and could influence the pharmacology of the most prescribed antihypertensive drugs in Barbados. Thiazide and thiazide-like diuretics, calcium channel blockers (CCBs), angiotensin-converting enzyme inhibitors (ACE-I), and angiotensin receptor blockers (ARBs) are the most prescribed antihypertensive drugs. Twenty-seven phytochemicals show dissociation constants (K (d)) < 10 muM with pharmacological drug targets. Catharanthus roseus (L.) G. Don, Phyllanthus niruri L., Petroselinum crispum (Mill.) Fuss, and Lantana involucrata L. contain various compounds that show high binding affinities in all experiments. Possible interactions could affect renal excretion (thiazide-like diuretics), CYP metabolism (CCBs), absorption (ACE-I), hepatic CYP, and phase II metabolism (ARB). Oleanolic acid shows high binding affinities to almost all protein targets. This study also reveals potential candidates for the drug targets: T-type Cav3.3 (psychiatric diseases), PEPT1/2 (influencing bioavailability), and BK channel (epilepsy). Twenty-seven of 30 phytochemicals from C. roseus (L.) G. Don (Madagascar periwinkle), P. niruri L. (Seed under leaf), P. crispum Mill. Fuss (Parsley), and L. involucrata L. (Rock sage) have potential binding affinities with pharmacological targets of frequently prescribed antihypertensive drugs in Barbados and are likely to cause drug interactions. Compounds that are similar to naringin (e.g., astragalin, rutin, and quercitrin) and compounds that bind to OATP1, PEPT1/2, and enzymes involved in the metabolism of CCBs may be clinically relevant for further research. There should be greater awareness of potential drug-herb interactions, and further in vitro and in vivo studies are needed to unravel the exact effects on the pharmacology.

Abstract: It is recognised that paediatric indications and age-appropriate formulations are required to ensure that paediatric populations receive appropriate pharmacotherapeutic treatment. The lack of information on dosing, efficacy and safety data (labelling) is a well-recognised problem for all diseases affecting children. For neglected tropical diseases, the fact that they affect to a large extent poor and marginalised populations in low- and middle-income countries means that there is a low economic return on investment into paediatric development activities compared to other diseases [e.g. human immunodeficiency virus (HIV)]. This review provides an introduction to issues affecting the availability and development of paediatric population-relevant data and appropriate formulations of drugs for NTDs. We are summarising why age-appropriate formulations are important to ensure treatment efficacy, safety and effectiveness, outline initiatives to increase the number of paediatric indications/labelling and age-appropriate formulations, provide an overview of publicly available information on the formulations of oral drugs for NTDs relative to age appropriateness and give an introduction to options for age-appropriate formulations. The review completes with 'case studies' of recently developed paediatric formulations for NTDs, complemented by case studies for fixed-dose combinations for HIV infection in children since such formulations have not been developed for NTDs.

Abstract: Covalent inhibition has emerged as a promising orthogonal approach for drug discovery, despite the significant challenge in achieving target specificity. To facilitate the structure-based rational design of target-specific covalent modulators, we developed an integrated computational protocol to curate covalent binders from the RCSB Protein Data Bank (PDB). Starting from the macromolecular crystallographic information files (mmCIF) in the PDB archive, covalent bond records, which indicate the side chain modification of amino acid residue by a covalent binder, were collected and cleaned. Then, residue-binder adducts, which are products of chemical reactions between targeted residues and covalent binders, were recovered with the help of the Chemical Component Dictionary in PDB. Finally, several strategies were employed to curate the pre-reaction forms of covalent binders from the adducts. Our curated CovBinderInPDB database contains 7375 covalent modifications in which 2189 unique covalent binders target nine types of amino acid residues (Cys, Lys, Ser, Asp, Glu, His, Met, Thr, and Tyr) from 3555 complex structures of 1170 unique protein chains. This database would set a solid foundation for developing and benchmarking computational strategies for covalent modulator design and is freely accessible at https://yzhang.hpc.nyu.edu/CovBinderInPDB.

Abstract: BACKGROUND: Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes. METHODS: Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets. RESULTS: In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases. CONCLUSIONS: Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.